Turning Privacy-preserving Mechanisms against Federated Learning. (arXiv:2305.05355v1 [cs.LG])

Recently, researchers have successfully employed Graph Neural Networks (GNNs)
to build enhanced recommender systems due to their capability to learn patterns
from the interaction between involved entities. In addition, previous studies
have investigated federated learning as the main solution to enable a native
privacy-preserving mechanism for the construction of global GNN models without
collecting sensitive data into a single computation unit. Still, privacy issues
may arise as the analysis of local model updates produced by the federated
clients can return …

addition build construction enable entities federated learning global learn main networks neural networks patterns privacy recommender systems researchers solution studies systems